Sentence examples for implement an approximation from inspiring English sources

In many real cases, there is a substantial amount of overlapping among the kernels with different deformations, so one can use a J ≪ R. Now we try to use the technique of deformable filtering to efficiently implement an approximation of the transposed SV-filtering, which is made by using the local PSFs as local IKs (see Section 2.1).

We also provide a method for implementing an approximation of the regularized inversion of a SV-matrix, under the assumption of having smoothly spatially varying kernels, and enough regularization.

We also have proposed a method for implementing an approximation of the filtering of a SV-matrix regularized inverted, under the assumption of having smoothly varying kernels, and enough regularization.

A local adaptation of the sensitivity of the photoreceptors to an average illumination level can also be seen as implementing an approximation of a logarithmic transformation, provided that both the baseline and the sensitivity regarding deviations from the baseline are adapted in a corresponding manner.

We implement an iterative approximation procedure based on this idea, and the procedure demonstrates the desirable anytime property in experiments.

Video systems depart from this principle and implement an engineering approximation.

Improvements on this theory could be obtained by implementing: A) a better approximation for the Green's function describing the fluence at the fluorophore site, which could be obtained by using high-order solutions of the perturbation theory [ 26, 27, 42] or exact RTE solutions [ 39]; B) the whole integral on the volume V′.

Facing the difficulty to optimize such nonorthogonal and nonlinear transforms, we implement a sparse approximation scheme inspired from the functional architecture of the primary visual cortex.

For our specific application of DOT, we implement a diffusion approximation to the radiative transport equation to model light propagation in highly scattering medium.

VBSSM implements an analytical approximation scheme to Bayesian state-space models and, unlike other related methods, does not take prior information (i.e. the seed network) into account for network reconstruction [ 84], such that networks were solely constructed from the experimental data.

In order to do so, we implement a non-parametric approximation of the heterogeneity distribution (Lindsay 1983; Heckman and Singer 1984).